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Open access Sep 2026

Artificial intelligence-based speech error detection to differentiate primary progressive aphasia variants

Abstract Artificial intelligence-based approaches to speech analysis have the potential to assist with objective speech error analysis in aphasia but off-the shelf tools often fail to detect speech errors due to prioritizing ‘fluent transcription’. Speech production errors (dysfluencies) are hallmark diagnostic feature...

J. Vonk, Jia-Chen Lian, G. Kurteff et al. · 0 citations

HuPER: A Human-Inspired Framework for Phonetic Perception

HuPER is a human-inspired framework that models phonetic perception as adaptive inference over acoustic-phonetics evidence and linguistic knowledge and is the first framework to enable adaptive, multi-path phonetic perception under diverse acoustic conditions.

Chen-Xu Guo, Jia-Chen Lian, Yi-Si Liu et al. · 4 citations · ⚡1

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